Texture synthesis for repairing damaged images

ABSTRACT

A method for generating texture includes (1) selecting a target patch to be filled in a image, (2) selecting a sample patch as a candidate for filling the target patch, (3) determining a first difference between a first area surrounding the target patch and a corresponding first area surrounding the sample patch, and a second difference between a second area surrounding the target patch and a corresponding second area surrounding the sample patch, (4) multiplying a larger of the first difference and the second difference with a first weight factor, and a smaller of the first difference and the second difference with a second weight factor, and (5) summing the weighted first difference and the weighted second difference as a distance between the target patch and the sample patch.

FIELD OF INVENTION

This invention relates to software for image inpainting and texture synthesis.

DESCRIPTION OF RELATED ART

Damaged images can be repaired by professional artists using a technique called inpainting. Various software inpainting techniques have been developed to undetectably repair images like professional artists. Bertalmio et al. proposes a method based on information propagation. M. Bertalmio, G. Sapior, V. Caselles, and C. Ballester, “Image Inpainiting,” Computer Graphics, Proceedings of SIGGRAPH, pp. 417–424, New Orleans, July 2000. It takes the known gray values of the points in the boundary of the damaged areas and propagates these gray values to the damaged area along the direction which has a small gradient. Chan et al. proposes a method that repairs image by solving the Partial Differential Equation (PDE). T. Chan, A. Marquina, and P. Mulet, “High-Order Total Variation-Based Image Restoration,” SIAM Journal on Scientific Computing, pp. 503–516, Vol. 22, No. 2, 2000.

Many damaged images look fine after being repaired by the aforementioned inpainting methods. However, these methods have a common shortcoming: all of them cannot retrieve the texture information of the image. This shortcoming is not very obvious when only a small area of the image is damaged. When a large area of the image is damaged, the result looks blurry without texture information and can be easily detected by the human eyes.

Texture synthesis methods can also repair damaged images, such as non-parametric sampling that creates texture using a Markov random fields (MRF) model. In the MRF model, the conditional Probability Distribution Function (PDF) of a point is calculated by using the neighbor points. The information of the damaged point is duplicated from a point which has the same conditional probability distribution. Efros et al. proposes a method that duplicates the information point by point. A. Efros and T. Leung, “Texutre Synthesis by Non-parametric Sampling,” In Proceedings of International Conference on Computer Vision, 1999. Liang et al. proposes a method based on patches (i.e., blocks). L. Liang, C E Liu, Y. Xu, B. Guo, and H. Shum, “Real-Time Texture Synthesis by Patch-Based Sampling,” Microsoft Research Technical Report MSR-TR-2001-40, March 2001.

The aforementioned texture synthesis methods can repair pure texture images well. However, the actual images in practice (such as natural images) often do not have features with repetitive texture. Furthermore, the texture features of these images are complicated by their environment such as lighting. Repairing images using the aforementioned texture synthesis methods without addressing these problems will not produce a satisfactory result.

Thus, what is needed is a method for repairing damaged images that addresses the shortcomings of the conventional inpainting and texture synthesis methods.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A, 1B, 2A, 2B, and 2C illustrate a conventional patch-based sampling algorithm.

FIG. 3 illustrates a method for filling an image in one embodiment of the invention.

FIGS. 4 and 6 illustrate the filling of an area in an image with a sample patch in one embodiment of the invention.

FIG. 5 illustrates a boundary zone of a patch divided into individual boundary areas in one embodiment of the invention.

FIGS. 7A, 7B, and 7C illustrate the improvement of the method of FIG. 3 over the conventional patch-based sampling algorithm.

Use of the same reference numbers in different figures indicates similar or identical elements.

SUMMARY

In one embodiment of the invention, a method for generating texture includes (1) selecting a target patch to be filled in an image, (2) selecting a sample patch as a candidate for filling the target patch, (3) determining a first difference between a first area surrounding the target patch and a corresponding first area surrounding the sample patch, and a second difference between a second area surrounding the target patch and a corresponding second area surrounding the sample patch, (4) multiplying a larger of the first difference and the second difference with a first weight factor, and a smaller of the first difference and the second difference with a second weight factor, and (5) summing the weighted first difference and the weighted second difference as a distance between the target patch and the sample patch.

DETAILED DESCRIPTION

Problems of a Conventional Patched-based Sampling Algorithm

Liang et al. discloses a patch-based sampling algorithm for synthesizing textures from a sample. This sampling algorithm is hereafter explained in reference to FIG. 1A where an image 2 has a target area 4 to be filled with textures from a sample texture 6. Target area 4 is divided into target patches B_(k) having a patch size of w by w (only one is labeled for clarity), where k is a variable. If the width or the height of target area 4 is not a multiple of w, the size of the last row and the last column of target patches is defined as: w*w₁, w₂*w,  (1) w₁=W mod w, w₂=H mod w,  (2) where W and H express the width and height of the target area, respectively, and mod is the function that calculates the residual of W or H divided by w.

Each target patch B_(k) includes a boundary zone E_(B) _(k) having a width w_(E) surrounding the target patch. Boundary zone E_(B) _(k) includes known texture from image 2 and derived texture from filling in other surrounding target patches with textures from sample texture 6.

Sample texture 6 is divided into sample patches B_((x,y)) (only one is labeled for clarity), where (x,y) denotes the left-lowest point of the sample patch. Sample patches B_((x,y)) and target patches B_(k) have the same size. Each sample patch B_((x,y)) includes a boundary zone E_(B) _((x,y)) surrounding the sample patch.

The corresponding points in boundary zones E_(B) _((x,y)) and E_(B) _(k) are compared to determine if a sample patch B_((x,y)) matches a target patch B_(k). If the distance (i.e., the difference) between the sample patch B_((x,y)) and the target patch B_(k) is less than a prescribed threshold, then that sample patch B_((x,y)) is placed in a set ψ_(B). The definition of set ψ_(B) is: ψ_(B) ={B _((x,y)) |d(E _(B) _(k) , E _(B) _((x,y)) )<d _(max)},  (3) where d is the distance between boundary zones E_(B) _((x,y)) and E_(B) _(k) Of sample patch B_((x,y)) and target patch B_(k), respectively, and d_(max) is the prescribed threshold. The definition of distance d between boundary zones E_(B) _(k) and E_(B) _((x,y)) is: $\begin{matrix} {{{d\left( {E_{B_{k}},E_{B_{({x,y})}}} \right)} = \left\lbrack {\frac{1}{A}{\sum\limits_{i = 1}^{A}\;\left( {p_{B_{k}}^{i} - p_{out}^{i}} \right)^{2}}} \right\rbrack^{1/2}},} & (4) \end{matrix}$ where A is the number of corresponding points in boundary zones E_(B) _((x,y)) and E_(B) _(k) , and p^(i) _(B) _(k) and p^(i) _(B) _((x,y)) denote the corresponding gray values of the corresponding points.

After all the sample patches B_((x,y)) in sample texture 6 are compared with a target patch B_(k), then a sample patch B_((x,y)) is randomly selected from set ψ_(B) and used to fill target patch B_(k). If set ψ_(B) is empty, then a sample patch B_((x,y)) with the smallest distance is selected to fill target patch B_(k). This process is repeated for each target patch B_(k) in target area 4 of image 2.

After filling in one target patch, the texture of that target patch becomes part of the known boundary zones of adjacent target patches. For example in FIG. 1B, a right area of a boundary zone E_(B) _(k+1) from a target patch B_(k+1) becomes a known left area of a boundary zone E_(B) _(k+2) from a target patch B_(k+2). After sample patches fill in the target patches, the overlapping areas of their boundary zones are blended.

FIG. 2A illustrates a target area 4 divided into nine target patches. As described above, the target patches can be filled one by one. FIG. 2B illustrates one order in which the target patches are filled in row by row. FIG. 2C illustrates one order in which the target patches are filled by an inward spiral order.

One disadvantage of Liang et al. is that formula 4 applies the same weight to all the areas that make up the boundary zones. For example, assuming a target patch having a boundary zone with known left and lower areas is compared with a sample patch having a boundary zone with a very similar or the same left area. The distance calculated may be smaller than the prescribed threshold because of the similarity between the left areas around the target and the sample patches even though the lower areas around the target and the sample patches are very different. When such a sample patch is used to fill the target patch, the lower portion of the sample patch may be greatly visible in the image. Furthermore, the selection of this sample patch will affect the subsequent target patches that are filled as the dissimilarity is propagated through subsequent matching operations.

Another disadvantage of Liang et al. is that it fails to compensate for asymmetrical lighting. Asymmetrical lighting in an image will give different gray values to the same texture. This makes it difficult to fill a target area with the proper texture because areas with similar gray values may have different textures while areas with the same texture may have different gray values. When a sample patch is pasted directly onto a target patch, then the sample patch may be visible in the image.

Improvement to the Patched-based Sampling Algorithm

FIG. 3 illustrates a method 10 for filling a target area 102 (FIG. 4) in an image 104 (FIG. 4) with textures from a sample area 106 (FIG. 4) in one embodiment of the invention. Target area 102 may be a damaged area that needs to be repaired while sample area 106 may be any undamaged area outside of target area 102 in image 104. Alternatively, sample area 106 can be another image or a group of sample patches. Method 10 can be implemented with software on a computer or any equivalents thereof.

In step 12, the computer optionally converts image 104 from a color image into a gray scale image. The computer can convert the color values into gray scale values as follows: $\begin{matrix} {{{I\left( {x,y} \right)} = \frac{{R\left( {x,y} \right)} + {G\left( {x,y} \right)} + {B\left( {x,y} \right)}}{3}},} & (5) \end{matrix}$ where I is the gray value of a point, and R, G, B are the color values of the point. This step may help to speed up the processing later described. Step 12 can be skipped if image 104 is a gray scale image from the start.

In step 14, the computer receives a target area 102 to be filled. Typically, target area 102 is designated by a user after the user visually inspects image 104.

In step 16, the computer divides target area 102 into target patches B_(k) with associated boundary zones E_(B) _(k) .

In step 18, the computer divides sample area 106 into target patches B_((x,y)) with associated boundary zones E_(B) _((x,y)) .

In step 20, the computer selects a target patch B_(k) from target area 102 to be matched with a sample patch. In one embodiment, the computer selects the target patch in an inward spiral order.

In step 22, the computer selects a sample patch B_((x,y)) from sample area 106 to be compared with target patch B_(k).

In steps 24 and 26, the computer determines the distance between the current target patch B_(k) and the current sample patch B_((x,y)). Specifically, in step 24, the computer determines the difference between the corresponding points in boundary zones E_(B) _(k) and E_(B) _((x,y)) . Unlike Liang et al., the computer divides the boundary zones into boundary areas and then determine the differences between the corresponding boundary areas as follows: $\begin{matrix} {{d_{n} = \left\lbrack {\frac{1}{A_{n}}{\sum\limits_{i = 1}^{A_{n}}\;\left( {p_{B_{i}}^{i} - p_{B_{({x,y})}}^{i}} \right)^{2}}} \right\rbrack^{1/2}},{2 \leq n \leq 4},} & (6) \end{matrix}$ where d_(n) is the difference of the nth pair of corresponding boundary areas in boundary zones E_(B) _(k) and E_(B) _((x,y)) , A_(n) is the number of corresponding points in the nth pair of corresponding boundary areas, and p^(i) _(B) _(k) and p^(i) _(B) _((x,y)) denote the corresponding gray values of the corresponding points. In one embodiment, the computer divides each boundary zone into a top boundary area B_(top), a left boudnary area B_(left), a bottom boundary area B_(bot), and a right boundary area B_(right) as shown in FIG. 5. Note that the four corners are part of two boundary areas and will be calculated twice in equation 6.

In step 26, the computer weighs the differences of the corresponding areas and then sums the weighted differences as follows: $\begin{matrix} {{d = {\sum\limits_{i = 1}^{n}\;{\alpha_{i}d_{i}}}},{0 < \alpha_{i} \leq 1},} & (7) \end{matrix}$ where d is the distance between target patch B_(k) and sample patch B_((x,y)), d_(i) is the difference of the ith pair of corresponding boundary areas in a descending sequence, α_(i) is the weight given to difference d_(i), and n is the total number of corresponding boundary areas. The value of α_(i) is determined by boundary width w_(E) and patch size w (where patch size w is typically determined by the size of the smallest repeated unit of texture known as textone, and w_(E) is typically $\left. {\left( {\left. \frac{1}{5} \right.\sim\frac{1}{4}} \right)w} \right).$ In one embodiment, value of α_(i) is determined as follows: $\begin{matrix} {{\alpha_{i} = \frac{1}{{sequence}\left( d_{i} \right)}},} & (8) \end{matrix}$ where the sequence is the descending sequence of the distances from the biggest to the smallest. Equation 8 thus gives different weights to different boundary areas, and the boundary area with the biggest distance has the weight 1.

In step 28, the computer determines if the distance between the current target patch B_(k) and the current sample patch B_((x,y)) is less than a prescribed threshold. If so, then step 28 is followed by step 30. Otherwise, step 28 is followed by step 32.

In step 30, the computer puts the current sample patch B_((x,y)) in a set ψ_(B), which contains all the sample patches that can be used to fill target patch B_(k).

In step 32, the computer determines if all the orientations of the current sample patch B_((x,y)) have been compared with the current target patch B_(k). This is because image 102 may have symmetrical areas, caused by reflection or other reasons, that can provide a good match for a target patch. Thus, different orientations of the current sample patch B_(k) are also compared for an acceptable match with the current target patch B_(k). In one embodiment, the current sample patch B_((x,y)) is orthogonally rotated three times from its original orientation to see if any of the other orientations provide an acceptable match with the current target patch B_(k). If all the orientations of the current sample patch B_((x,y)) have been tried, then step 32 is followed by step 36. Otherwise step 32 is followed by step 34.

In step 34, the computer rotates the current sample patch B_((x,y)). Step 34 is followed by step 24 and the newly rotated sample patch B_((x,y)) is compared with the current target patch B_(k). Method 10 repeats this loop until all the orientations of the current sample patch B_((x,y)) have been compared to the current target patch B_(k).

In step 36, the computer determines if the last sample patch B_((x,y)) in sample area 106 has been compared with the current target patch B_(k). If so, then step 36 is followed by 38. Otherwise step 36 is followed by step 22 and another sample patch B_((x,y)) is selected. Method 10 thus repeats this loop until all the sample patches B_((x,y)) in sample area 106 have been compared to the current target patch B_(k).

In step 38, the computer randomly selects a sample patch B_((x,y)) from set ψ_(B) to fill the current target patch B_(k). If set ψ_(B) is empty, then the computer selects the sample patch B_((x,y)) with the smallest distance to fill the current target patch B_(k).

In step 40, the computer adjusts the gray values of sample block B_((x,y)) to make them look natural with the boundary values of the current target patch B_(k) and at the same time keep their texture features. Assume an image g is the selected sample patch and an image f is the boundary around the current target patch. It is desired to generate a new sample patch u that has the same texture features as selected sample patch g (i.e., have the same color gradient) and has the same color at its boundary f.

In order to keep the gradient of the selected sample patch g, the new sample patch u should satisfy the functions: $\begin{matrix} {{\frac{\partial u}{\partial x} = \frac{\partial g}{\partial x}},{\frac{\partial u}{\partial y} = \frac{\partial g}{\partial y}}} & (9) \end{matrix}$ where $\frac{\partial u}{\partial x}$ is the gradient of the new sample patch u in x direction at point (x,y), $\frac{\partial u}{\partial y}$ is the gradient of the new sample patch u in y direction at point (x,y), $\frac{\partial g}{\partial x}$ is the gradient of the selected sample patch g in x direction at point (x,y), and $\frac{\partial g}{\partial y}$ is the gradient of the selected sample patch g in y direction at point (x,y). Equation 9 can be rewritten as: $\begin{matrix} {\left. \begin{matrix} {\left( {\frac{\partial u}{\partial x} - \frac{\partial g}{\partial x}} \right)^{2} = 0} \\ {\left( {\frac{\partial u}{\partial y} - \frac{\partial g}{\partial y}} \right)^{2} = 0} \end{matrix}\Leftrightarrow{\left( {\frac{\partial u}{\partial x} - \frac{\partial g}{\partial x}} \right)^{2} + \left( {\frac{\partial u}{\partial y} - \frac{\partial g}{\partial y}} \right)^{2}} \right. = 0.} & (10) \end{matrix}$

In order to make the new sample patch u have the same color at its boundary area f, the new sample patch u should satisfy the function: u=f  (11) Equation 11 can be rewritten as: u=f

(u−f)=0

(u−f)²=0.  (12) Equations 10 and 12 can be combined into a single equation as follows: $\begin{matrix} {{\left. \begin{matrix} {\left( {u - f} \right)^{2} = 0} \\ {{\left( {\frac{\partial u}{\partial x} - \frac{\partial g}{\partial x}} \right)^{2} + \left( {\frac{\partial u}{\partial y} - \frac{\partial g}{\partial y}} \right)^{2}} = 0} \end{matrix}\Leftrightarrow{\left( {u - f} \right)^{2} + \left( {\frac{\partial u}{\partial x} - \frac{\partial g}{\partial x}} \right)^{2} + \left( {\frac{\partial u}{\partial y} - \frac{\partial g}{\partial y}} \right)^{2}} \right. = 0},} & (13) \end{matrix}$ In other words, the new sample patch u should satisfy the equation 13 at point (x,y). Satisfying these conditions for the entire new sample patch u, equation 13 is rewritten as: $\begin{matrix} {{{\sum\limits_{x\;{\varepsilon\Omega}}^{\;}\;{\sum\limits_{y\;{\varepsilon\Omega}}^{\;}\;\left( {\frac{\partial u}{\partial x} - \frac{\partial g}{\partial x}} \right)^{2}}} + \left( {\frac{\partial u}{\partial y} - \frac{\partial g}{\partial y}} \right)^{2} + \left( {u - f} \right)^{2}} = 0.} & (14) \end{matrix}$ where Ω is the area to be pasted with the new sample patch u. If equation 14 is written in continuous form, it becomes: $\begin{matrix} {{\int_{\Omega}^{\;}{\left( {\left( {\frac{\partial u}{\partial x} - \frac{\partial g}{\partial x}} \right)^{2} + \left( {\frac{\partial u}{\partial y} - \frac{\partial g}{\partial y}} \right)^{2} + \left( {u - f} \right)^{2}} \right){\mathbb{d}x}{\mathbb{d}y}}} = 0.} & (15) \end{matrix}$ As there is no solution of the new sample patch u that satisfies equation 15, the closest solution for the new sample patch u is determined by defining a function J(u) as follows: $\begin{matrix} {{{J(u)} = {{\int_{\Omega}^{\;}{\left( {\left( {\frac{\partial u}{\partial x} - \frac{\partial g}{\partial x}} \right)^{2} + \left( {\frac{\partial u}{\partial y} - \frac{\partial g}{\partial y}} \right)^{2}} \right){\mathbb{d}\left( {x,y} \right)}}} + {\lambda{\int_{\Omega}{\left( {u - f} \right)^{2}{\mathbb{d}\left( {x,y} \right)}}}}}},} & (16) \end{matrix}$ where λ is the weight given between satisfying the boundary condition against satisfying the gradient condition, and d(x,y) is dxdy. Conventional minimizing methods, such as the Steepest Descent and Iteration, can be used to minimize function J(u).

In step 42, the computer fills the current target patch B_(k) with the adjusted sample patch B_((x,y)). Unlike Liang et al., where the boundary zone of the sample patch is also filled in to overlap with the known areas outside of the target patch and the areas derived from the filling of other target patches, the computer only fills in the selected sample patch B_((x,y)) without its boundary zone E_(B) _((x,y)) into the current target patch B_(k). As shown in FIG. 6, any area within the selected sample patch B_((x,y)) can become the boundary zone of another target patch B_(k+1) to be filled. Specifically, part of sample patch B_((x,y)) becomes a left area B_(left) of boundary zone E_(B) _(k+1) of target batch B_(k+1).

In step 44, the computer determines if the current target patch B_(k) is the last target patch in target area 102. If so, then step 44 is followed by step 46. Otherwise step 44 is followed by step 20 and another target patch B_(k) is selected. Method 10 thus repeats this loop until all the target patches B_(k) in target area 102 have been filled.

In step 46, the computer optionally converts image 102 from a gray scale image into a color image. In one embodiment, the computer imposes the color characteristics of the original color image 102 onto the gray scale image 102 using the method disclosed by Reinhard et al. E. Reinhard, M. Ashikhmin, B. Gooch, P. Shirley, “Color Transfer between Images,” IEEE Computer Graphics and Applications, Vol. 21, No. 5, September/October 2001.

As described above, method 10 provides many improvements over the conventional patched-based sampling algorithm disclosed by Liang et al. First, method 10 weighs the different areas of the boundary zones differently when calculating the distance between a sample patch and a target patch. Second, method 10 adjusts the gray values of the sample patch to better match the target patch. Third, method 10 compares the sample patch at various orientations to better match the target patch. Fourth, method 10 converts color images to gray scale images to improve processing speed.

FIG. 7A illustrates an image 202 having a target area 204 to be filled with texture from a sample area within image 202 but outside of target area 204. FIG. 7B illustrates image 202 after it has been filled by the conventional patched-based sampling algorithm disclosed by Liang et al. A patch size of 20*20 pixels and a boundary width of 4 pixels were used. A 600*400 pixels image 202 and a 100*40 pixels target area 204 were used. As the figure shows, the filled area is still visible to the human eyes. FIG. 7C illustrates image 202 after it has been filled by method 10 (FIG. 3) using the above parameters in one embodiment of the invention. As the figure shows, the filled area is not visible to the human eyes.

Various other adaptations and combinations of features of the embodiments disclosed are within the scope of the invention. Numerous embodiments are encompassed by the following claims. 

1. A method, comprising: selecting a target patch to be filled in an image; selecting a sample patch as a candidate for filling the target patch; determining a first difference between a first area surrounding the target patch and a corresponding first area surrounding the sample patch, and a second difference between a second area surrounding the target patch and a corresponding second area surrounding the sample patch; multiplying a larger of the first difference and the second difference with a first weight factor, and a smaller of the first difference and the second difference with a second weight factor; and summing the weighted first difference and the weighted second difference as a distance between the target patch and the sample patch.
 2. The method of claim 1, wherein the sample patch is selected from the image.
 3. The method of claim 1, wherein said determining a first difference and said determining a second difference comprise: ${d_{n} = \left\lbrack {\frac{1}{A_{n}}{\sum\limits_{i = 1}^{A_{n}}\;\left( {p_{B_{i}}^{i} - p_{B_{({x,y})}}^{i}} \right)^{2}}} \right\rbrack^{1/2}},$ where d_(n) is the difference of an nth pair of corresponding areas, A_(n) is the number of corresponding points in the nth pair of corresponding areas, and p^(i) _(B) _(k) and p^(i) _(B) _((x,y)) are the corresponding gray values of the corresponding points in the target patch and the sample patch, respectively.
 4. The method of claim 3, wherein said multiplying and said summing comprise: ${d = {\sum\limits_{i = 1}^{n}\;{\alpha_{i}d_{i}}}},{\alpha_{i} = \frac{1}{{sequence}\left( d_{i} \right)}},$ where d is the distance between the target patch and the sample patch, d_(i) is the difference of the ith pair of corresponding boundary areas in a descending sequence, α_(i) is the weight given to the difference d_(i), and n is the total number of corresponding boundary areas.
 5. The method of claim 1, further comprising: adjusting pixel values of the sample patch to match the first and the second areas surrounding the target patch; and filling the target patch with the adjusted sample patch.
 6. The method of claim 5, wherein said adjusting values of the sample patch comprises determining an adjusted sample patch that minimizes the following equation: ${{J(u)} = {{\int_{\Omega}^{\;}{\left( {\left( {\frac{\partial u}{\partial x} - \frac{\partial g}{\partial x}} \right)^{2} + \left( {\frac{\partial u}{\partial y} - \frac{\partial g}{\partial y}} \right)^{2}} \right){\mathbb{d}\left( {x,y} \right)}}} + {\lambda{\int_{\Omega}{\left( {u - f} \right)^{2}{\mathbb{d}\left( {x,y} \right)}}}}}},$ where u is the adjusted sample patch, g is the sample patch, f includes the first and the second areas, Ω is an area to be filled with the adjusted sample patch u, $\frac{\partial u}{\partial x}$ is a gradient of the adjusted sample patch u in x direction at point (x,y), $\frac{\partial u}{\partial y}$ is a gradient of the adjusted sample patch u in y direction at point (x,y), $\frac{\partial g}{\partial x}$ is a gradient of the sample patch g in x direction at point (x,y), and $\frac{\partial g}{\partial y}$ is a gradient of the sample patch image g in y direction at point (x,y), and λ is a weight factor.
 7. The method of claim 1, further comprising: rotating the sample patch; determining another first difference between the first area surrounding the target patch and another corresponding first area surrounding the sample patch after said rotating, and another second difference between the second area surrounding the target patch and another corresponding second area surrounding the sample patch after said rotating; multiplying the larger of said another first difference and said another second difference with the first weight factor, and the smaller of said another first difference and said another second difference with the second weight factor; summing the weighted another first difference and the weighted another second difference as another distance between the target patch and the sample patch after said rotating.
 8. The method of claim 7, further comprising: if the distance between the target patch and the sample patch is less than a threshold, saving the sample patch in a set of sample patches that can be used fill the target patch; if said another distance between the target patch and the sample patch after said rotating is less than the threshold, saving said another sample patch in the set of sample patches that can be used fill the target patch; and selecting one sample patch from the set of sample patches and filling the target patch with said one sample patch.
 9. The method of claim 1, further comprising, prior to said selecting a target patch, said selecting a sample patch, said determining, said multiplying, and said summing: converting the image from color to gray.
 10. The method of claim 9, further comprising, after said converting the image from color to gray, said selecting a target patch, said selecting a sample patch, said determining, said multiplying, and said summing: converting the filled image from gray to color.
 11. The method of claim 1, further comprising: if the distance between the target patch and the sample patch is less than a threshold, saving the sample patch in a set of sample patches that can be used fill the target patch; selecting another sample patch and repeating said determining, said multiplying, and said summing for said another sample patch to determine another distance between the target patch and said another sample patch; if said another distance between the target patch and said another sample patch is less than the threshold, saving said another sample patch in the set of sample patches that can be used fill the target patch; and selecting one sample patch from the set of sample patches and filling the target patch with said one sample patch.
 12. A method, comprising: selecting a target patch to be filled in the image; selecting a sample patch as a candidate for filling the target patch; determining a first difference between a first area surrounding the target patch and a corresponding first area surrounding the sample patch, and a second difference between a second area surrounding the target patch and a corresponding second area surrounding the sample patch; multiplying a larger of the first difference and the second difference with a first weight factor, and a smaller of the first difference and the second difference with a second weight factor; summing the weighted first difference and the weighted second difference as a distance between the target patch and the sample patch; if the distance between the target patch and the sample patch is less than a threshold, saving the sample patch in a set of sample patches that can be used fill the target patch; selecting another sample patch and repeating said determining, said multiplying, and said summing for said another sample patch to determine another distance between the target patch and said another sample patch; if said another distance between the target patch and said another sample patch is less than the threshold, saving said another sample patch in the set of sample patches that can be used fill the target patch; selecting one sample patch from the set of sample patches; adjusting pixel values of the selected sample patch to match the first and the second areas surrounding the target patch; and filling the target patch with the adjusted sample patch. 